Remote: Yes

Willing to relocate: Yes

Technologies: Python, Deep Learning (PyTorch), NLP, Graph Knowledge Systems, Vector Search (Cosine Similarity), PostgreSQL, React/Svelte.

Résumé/CV: https://www.linkedin.com/in/rodrigo-heck-7280a218a/

Email: rodrigo.heck29@gmail.com

Hi HN, I’m Rodrigo. I’m a software engineer working at the intersection of applied ML and scalable systems. Lately, I’ve been obsessed with solving the "context window" problem through graph-based knowledge representations. My recent work focuses on treating graphs not just as data stores, but as a method for information compression and long-term memory permanence in AI agents. I develop pipelines that integrate LLMs with structured memory to allow for more efficient knowledge generalization and recall.

Key areas of expertise:

1. AI Memory Architectures: Building persistent, graph-oriented systems for reasoning and long-term retrieval.

2. ML Systems Engineering: Developing TTS systems, semantic search tools, and RAG pipelines that go beyond simple vector lookups.

3. Full-Stack Foundations: Bridging the gap between a PyTorch model and a production-ready Svelte/React interface, backed by robust Linux/Postgres infrastructure.

I’m looking for a role where I can contribute to the "Next Step" of LLM integration—moving past simple chat interfaces toward systems with true persistent memory and structured reasoning.

Remote: Yes

Willing to relocate: Sure

Technologies: Deep Learning (PyTorch), ReactJS, Svelte, Natural Language Processing (NLP), Python (Flask, OpenAI API), Graph Knowledge Systems, Vector Search (Cosine Similarity), PostgreSQL, Linux System Administration

Résumé/CV: https://www.toptal.com/resume/rodrigo-heck

Email: rodrigo.heck@toptal.com

Lately, I’ve been exploring graph-based knowledge representations as a method for information compression and long-term memory permanence in AI systems — developing pipelines that integrate LLMs with structured memory to retain and generalize knowledge efficiently.

My background bridges applied machine learning and software engineering, from building text-to-speech systems and semantic retrieval tools to experimenting with persistent, graph-oriented architectures for reasoning and recall. I’m looking for opportunities at the intersection of AI systems engineering, knowledge representation, and scalable memory architectures.